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will apply state-of-the-art machine learning algorithms and custom disease-relevant genomic datasets (e.g., coronary artery single-nucleus chromatin accessibility and RNA sequencing) to develop targeted
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learning algorithms on graphs to model, characterize, predict, and design the thermal and physical behaviors of diverse material systems. Responsibilities also include the development of software codes
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correlations to investigate the underlying causes of disease and the molecular baseline of healthy tissue. The postdoctoral associate will be responsible for designing and implementing an algorithm
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